Image processing device and image processing method

ABSTRACT

An image processing device includes an extraction process executing unit that extracts a 3-dimensional initial region satisfying a predetermined condition from the volume data, a region correction process executing unit that extracts a 3-dimensional corrected region by performing a correction process on the initial region, and a visualization process executing unit that generates a plurality of cross-sectional diagrams of the 3-dimensional image from the volume data and outputs at least some of the plurality of cross-sectional diagrams. When the initial region is displayed, one or more cross-sectional diagrams having voxels in the initial region are outputted so that the voxels included in the initial region are distinguishable from other regions. When the corrected region is displayed, one or more cross-sectional diagrams having voxels in the corrected region are outputted so that the voxels included in the corrected region are distinguishable from other regions.

TECHNICAL FIELD

The present invention relates to an image processing technology formanually correcting a 3-dimensional region set in 3-dimensional volumedata.

BACKGROUND ART

In diagnosis using a medical image inspection device typified by anX-ray CT (X-ray Computed Tomography) device or an MRI (MagneticResonance Imaging) device, a visualization process is executed on acaptured 3-dimensional medical image (hereinafter also referred to as“medical image volume data”) using a plurality of medical imageprocessing algorithms and an obtained result is used as diagnosisassistance in some cases. In regard to the medical image processingalgorithms used here, there are a plurality of classifications ofprocessing methods according to, for example, cases in which only dataof a necessary tissue part is extracted from input medical image volumedata for display and in which an edge of an image is emphasized anddisplayed.

Here, when a medical image processing algorithm is automatically appliedand an image is displayed, it is very difficult to automatically applyall of the processes in consideration of safety or accuracy, and finallyit is necessary for a doctor or an engineer to execute confirmation orcorrection in most cases. However, with recent advance in technologiesof medical image capturing devices, the number of tomographic images(hereinafter also referred to as “slice images”) which can be acquiredonce has been increased at an accelerated pace, and thus an amount ofmedical image volume data output as a photography result isconsiderable. Thus, when the amount of medical image volume data isconsiderable, the above-mentioned work of executing the confirmation andcorrection by humans is particularly very expensive in terms of a loadimposed on a doctor or an engineer or human cost. In order to reducesuch a load, accuracy and validity of a portion subjected to automaticprocessing are required to be improved as much as possible, andsimultaneously to improve simplicity or efficiency of a portionsubjected to manual processing is also an important task nowadays.

PTL 1 discloses a method of correcting a contour line of a 3-dimensionalregion into a free curve, and specifically, a method of reflecting amovement distance and a movement time of a pointing device to correctionof a curve.

PTL 2 suggests a method of setting a guide region separately apart froma region desired to be extracted and performing correction on theextracted region so as to enter the range of the guide region.

CITATION LIST Patent Literature

PTL 1: U.S. Pat. No. 4,444,346

PTL 2: U.S. Pat. No. 4,394,127

SUMMARY OF INVENTION Technical Problem

When an automatically extracted region is further corrected manually,technologies which have been suggested ever as methods of executingmanual correction simply have the following problems. That is, thetechnologies have problems in that even in a case where a plurality ofpoints desired to be corrected are present on a contour line, the pointscan be corrected only one by one; there is a possibility of a correctedcurve (contour line) becoming considerably different from original imageinformation since the curve is corrected into a free curve; the amountof data may be increased according to the amount of correction points ina case where a progress of change is stored; and a region serving as aguide has to be set in addition to an extraction target region.

Accordingly, it is demanded to provide an image processing method, animage processing device, and a program capable of executing correctionas simply as possible when manual correction is executed on a3-dimensional region.

Solution to Problem

An image processing device executing image processing on volume data ofa 3-dimensional image includes: an extraction process executing unitthat extracts a 3-dimensional initial region satisfying a conditiongiven in advance from the volume data; a region correction processexecuting unit that extracts a 3-dimensional corrected region byperforming a correction process on the initial region; and avisualization process executing unit that generates a plurality ofcross-sectional diagrams of the 3-dimensional image from the volume dataand outputs at least some of the plurality of cross-sectional diagrams.

When the initial region is displayed, the visualization processexecuting unit selects one or more cross-sectional diagrams havingvoxels included in the initial region from the plurality ofcross-sectional diagrams generated by the visualization processexecuting unit, and outputs the selected one or more cross-sectionaldiagrams so that the voxels included in the initial region aredistinguishable from other regions.

When the corrected region is displayed, the visualization processexecuting unit selects one or more cross-sectional diagrams havingvoxels included in the corrected region from the plurality ofcross-sectional diagrams generated by the visualization processexecuting unit, and outputs the selected one or more cross-sectionaldiagrams so that the voxels included in the corrected region aredistinguishable from other regions.

Advantageous Effects of Invention

It is possible to execute correction on a 3-dimensional region ofinterest extracted automatically from 3-dimensional volume data in asimple manner and in a short time.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a system configuration diagram illustrating an example of theconfiguration of an image processing device.

FIG. 2 is a flowchart illustrating an example of a region extraction andcorrection process executed by an image processing algorithm executingunit 21.

FIG. 3 is an image diagram illustrating an example of screen displaydisplayed on a display device 12 by a 3-dimensional region visualizationprocess executed by a visualization process executing unit 31.

FIG. 4 is an image diagram illustrating an example of a region correctedfrom an initial region extracted by an automatic extraction processexecuting unit 34 by changing a parameter of an extraction algorithm bya region correction process executing unit 32.

FIG. 5 is a parameter table stored in the region correction processexecuting unit 32 and is table illustrating an example of a case inwhich the values of parameters themselves are set.

FIG. 6 is a parameter table stored in the region correction processexecuting unit 32 and is a table illustrating an example of a case inwhich the values of parameters are set in the forms of formulae.

FIG. 7 is a table illustrating an example of a correction process itemtable stored in the region correction process executing unit 32.

FIG. 8 is an image diagram illustrating examples of regions corrected bythe region correction process executing unit 32 from initial regionsextracted by the automatic extraction process executing unit 34,including a correction procedure.

FIG. 9 is an image diagram illustrating examples of a plurality ofcorrected regions corrected by the region correction process executingunit 32.

DESCRIPTION OF EMBODIMENTS

Hereinafter, an image processing device, an image processing method, anda program will be described in detail with reference to the drawings.

FIG. 1 is a diagram illustrating an example of the configuration of animage processing device.

As illustrated in FIG. 1, a system includes an external storage device10 that stores a 3-dimensional image (hereinafter also referred to asvolume data) or data related to the 3-dimensional image, an imageprocessing device 11 that executes image processing on the volume datacaptured and restructured by an X-ray CT device, an MRI device, or thelike, a display device 12 that displays an image subjected to the imageprocessing, and an input device 13 that inputs an instruction such asstart of each process or a manipulation on a screen or an extractionresult.

The image processing device 11 includes an internal memory 20 thatstores the volume data or information related to the volume data, animage processing algorithm executing unit 21, an input manipulationacquiring unit 22. The image processing algorithm executing unit 21 isrealized by program processing by a CPU (Central Processing Unit) or adedicated circuit included in the image processing device 11. Further,the internal memory 20 and a storage unit (not illustrated) that storesa program for realizing the functions of the image processing device 11are configured by a storage medium such as RAM (Random Access Memory),ROM (Read-Only Memory), an HDD (Hard Disk Drive), and flash memory.

The image processing algorithm executing unit 21 includes avisualization process executing unit 31 that executes a visualizationprocess on the volume data and displays the volume data on the displaydevice, an extracted region storage unit 33 that stores informationregarding an extracted region, a region correction process executingunit 32 that performs a correction process on an automatically extractedregion, and an automatic extraction process executing unit 34 thatperforms an automatic extraction process on the volume data.

An overall flow at the time of correction of an automatic extractionprocess result will be described with reference to FIG. 2.

First, a 3-dimensional automatic extraction region correction process isstarted in response to an input from the input device 13 or aninstruction from the system (Step 40).

The volume data transmitted from the external storage device 10 to theinternal memory 20 is input to the image processing algorithm executingunit 21, and then the automatic extraction process executing unit 34executes the automatic extraction process on the input volume data (Step41). The automatic extraction process executed by the automaticextraction process executing unit 34 indicates, for example, a processof a region growing method or a LevelSet method. The region growingmethod mentioned here is a scheme of growing a region from a seed pointequal to or greater than one voxel designated automatically or manuallywhile determining whether adjacent voxels are suitable for a growingcondition and of extracting a desired 3-dimensional region from thevolume data. The LevelSet is a scheme of extracting a desired3-dimensional region from the volume data by detecting a boundary planeof an object present within a 2-dimensional space through time evolutionon a curved plane defined in a 3-dimensional space.

The extracted region storage unit 33 stores the 3-dimensional region(also referred to as an extracted region) automatically extracted inStep 41 first as an initial region (Step 42).

The visualization process executing unit 31 performs a visualizationprocess on the initial region stored in the extracted region storageunit 33 (Step 43). The visualization process executing unit 31 uses a3-dimensional visualization scheme, such as Multi Planar Reformat (MPR)of displaying a cross-sectional diagram of a defined cross-section suchas Axial, Sagittal, or Coronal or any cross-section, or Curved MultiPlanar Reformat (CMPR) of displaying a cross-sectional diagram of anycurved plane, on the input volume data. A plurality of cross-sectionaldiagrams of a 3-dimensional image (3-dimensional region) expressed bythe input volume data are consequently generated. At this time, theextracted 3-dimensional region can be visually viewed by differentlycoloring the 3-dimensional regions extracted by the automatic extractionprocess executing unit 34 and other regions in the input volume data.

In order to accurately confirm the extracted 3-dimensional region, it isimportant to confirm which voxels are extracted as the initial region ona cross-section (hereinafter also referred to as a slice). Thus, in Step43, a method of displaying all of the voxels set as the initial regionon a 2-dimensional plane is used in order to accurately comprehend a3-dimensional shape on a 2-dimensional plane. Specifically, for example,there is a method of storing slices having the voxels included in theextracted region at the time of generating a plurality of slices fromthe input volume data by the visualization process executing unit 31,and outputting and displaying data of the stored slices by the displaydevice 12. At this time, it is preferable that the voxels set as theextracted region in the displayed slice be colored. Also, there may beused a method of storing the cross-section including the voxels set asthe extracted region among a plurality of cross-sections parallel to aplane generated by the CMPR and arbitrarily set, by the visualizationprocess executing unit 31, and displaying the stored cross-section bythe display device 12.

In the state displayed in this way, the extracted region is confirmed(Step 44). When regions of interest are extracted as the initial regionswithout excess or deficiency, the extraction process ends at this timepoint (Step 47).

When there is excess or deficiency, a process of correcting thedisplayed regions (for example, the initial regions) is performed (Step45). Examples of the correction process used herein include a method ofchanging a parameter of the above-mentioned automatic extraction processand contour correction according a morphological process or a contourcorrection method. Here, a user does not correct a contour freely, but aregion obtained by correcting luminance information of the initialregion or the input volume data faithfully to some extent is set as anew extracted region. The new extracted region is stored as a correctedregion in the extracted region storage unit 33.

The visualization process executing unit 31 executes the visualizationprocess on the corrected region extracted in Step 45 in the same manneras that of Step 43 and displays the corrected region (Step 46).

Thereafter, Steps 44 to 46 are repeated, and then the extraction processends when it is determined in Step 44 that the regions of interest areextracted without excess or deficiency (Step 47).

When a 3-dimensional region is displayed as a set of a plurality of2-dimensional cross-section images, there are a plurality of3-dimensional regions stored in the extracted region storage unit 33 andit is necessary to change display in a 3-dimensional region differentfrom the currently displayed 3-dimensional region in some cases. Forexample, in the example of FIG. 2, the visualization display has beenexecuted at the initial region, but a case in which a target of thevisualization process is changed to the corrected region can beconsidered. In this case, when the angles of the cross-sectionsgenerated from the original volume data and the intervals between theslices are the same but the size of a 3-dimensional region instructed tobe displayed is different from that of the currently displayed3-dimensional region, the number of 2-dimensional cross-sections to bedisplayed may be different. Even in this case, the visualization processexecuting unit 31 automatically changes the number of 2-dimensionalcross-sections to be displayed, and selects and displays a set of the2-dimensional cross-sections so that the entire 3-dimensional region tobe displayed can be covered. As described above, this is becausewhenever the visualization process executing unit 31 executes thevisualization process, the slice including the extracted region isstored and the data of the stored slice is displayed. The details of acase in which a 3-dimensional region is displayed as a set of2-dimensional cross-sections will be described with reference to FIG. 3.FIG. 3 is an image diagram illustrating an example of screen displaydisplayed on the display device 12 through a 3-dimensional regionvisualization process executed by the visualization process executingunit 31. In FIG. 3, a hatched portion is the extracted region.

The left of FIG. 3 illustrates display of the initial regions. Theinitial regions cover three slices among the plurality of slicesgenerated from the input volume data. Accordingly, data of the threeslices are displayed.

Display of corrected regions 1 in the case in which correction isdetermined to be necessary in the confirmation executed in Step 44 isillustrated in FIG. 3. A case in which the size of the corrected region1 in a slice direction is greater than that of the initial region isexemplified here. Since the corrected regions 1 each have a size whichis greater than the initial region (that is, since corrected region 1has a size greater than that of the initial region and covers fiveslices) by two slices, the number of slices to be automaticallydisplayed is changed so that all of corrected regions 1 are displayed.

It is determined that it is necessary to correct the corrected regions1, and then display of corrected regions 2 obtained through correctionof the corrected regions 1 is illustrated on the right of FIG. 3. Thesize of the corrected region 2 is smaller than that of the correctedregion 1 and the corrected region 2 covers four slices, and thus thenumber of displayed slices is reduced for display.

Here, when the display in Steps 43 and 46 is executed, a 3-dimensionallyvisualized image can also be displayed simultaneously for the purposefor comprehending the 3-dimensional shape on a 2-dimensional screen tosome extent, in addition to the 2-dimensional slice display describedabove. Examples of a method of generating a 3-dimensionally visualizedimage include Volume Rendering (VR) of setting transparency from voxelvalues, adding light on the assumption of absorption and diffusion ofthe light with respect to voxels on each line of sight, and executingstereoscopic display and Maximum Intensity Projection (MIP) ofprojecting the maximum voxel value of the voxels on each line of sight.When the methods are used, an accurate shape and a rough 3-dimensionalshape can be comprehended on one screen during the correction from theinitial region.

Next, a specific example of the region correction process in Step 45will be described. The initial region is an image extracted in a shapewhich is determined to be general and plausible in the pixels of theinput volume data or its part, but another extracted shape may bedesirable in some cases depending on image characteristics of the inputvolume data or a use scene of the system.

Here, a case in which a tumor produced in an organ having consistent CTvalues is extracted will be described as one example. In image diagnosisusing a CT image, a contrast medium is used in many cases to distinguisha healthy substantial part of an organ from a lesion part such as atumor on an image. The contrast medium is a specific medical agentinjected into blood from a blood vessel. Since a time at which acontrast medium mixed in blood reaches a tumor is different from a timeat which the contrast medium flows from the tumor to a substantialorgan, the luminance of the tumor is known to be different from theluminance of the substantial organ depending on a time from theinjection of the contrast medium to photography.

For example, as a tumor region extraction process executed on an imageof which luminance is lower than that of a substantial organ, a portionwith low luminance is generally extracted as a tumor. However, a portionwith lower luminance is present in the tumor in some case depending onthe property of a tumor. As the reason, for example, a case in whichthere is unevenness in expansion of a contrast medium in a tumor, a casein which a thrombus is present in a tumor, or a case in which a necroticregion is present in a tumor is considered. In this case, when anextraction process is executed by setting extraction of portions withlow luminance as a basic algorithm, only regions with the lowestluminance are extracted as initial regions in many cases. In the case,however, only a region with the lowest luminance is not extracted, but aregion with an intermediate value between a lowest luminance region anda high luminance region (substantial organ) as a pixel value is alsopreferably extracted as a tumor.

According to the present scheme, a desired extracted region can beobtained by inputting the fact that there is excess or deficiency fromthe input device 13 at the time of the confirmation of the initialregion (Step 44 of FIG. 2), extracting a next plausible region in theextracted region storage unit by the region correction executing unit(Step 45 of FIG. 2), and suggesting the extracted region to thevisualization process executing unit 31 (Step 46 of FIG. 2). An exampleof this correction process will be described with reference to FIG. 4.

FIG. 4 is an image diagram illustrating an example in which the initialregion extracted by the automatic extraction process executing unit 34is corrected by changing a parameter of an extraction algorithm by theregion correction process executing unit 32.

Here, an image with luminance on FIG. 4 is assumed to be an input image.For the description, regions 1, 2, and 3 are assumed to have singleluminance in the respective regions, and an automatic extractionalgorithm is assumed to be an algorithm in which a region with luminancelower than preset luminance is set as an extracted region through abinarization process using a threshold value. Here, it is assumed thatthe regions 1, 2, and 3 are configured by voxels with luminance 1,luminance 2, and luminance 3, respectively, and the luminance 1 isgreater than the luminance 2 and the luminance 2 is greater than theluminance 3.

When a preset initial threshold value is greater than the region 3 andis less than the region 2, only the region 3, that is, an initial regionA, is extracted as the result of the automatic extraction process, asillustrated on the lower left of FIG. 4. For example, when it is assumedthat the region 2 is the entire region of an organ, the region 3 is theregion of a lesion part, and the lesion part is extracted for thepurpose of a process, a target region is extracted without excess ordeficiency in regard to the initial region A. Thus, the process endshere. However, when the region 1 is shown as a part of an organ and theregions 2 and 3 are lesion parts, the initial region A is extractedmerely as a part of the lesion part. Therefore, it is necessary toexecute a region correction process. Accordingly, a parameter of anextraction algorithm is changed in order to extract a plausible regionas a next extraction result of the initial region A. That is, since theextraction algorithm is here a binarization process using a thresholdvalue, by changing the initial threshold value to a correction thresholdvalue A greater than the luminance 2 and less than the luminance 1, acorrected region B illustrated on the lower right of FIG. 4 can beobtained, and thus a target region can be extracted without excess ordeficiency.

The change in the luminance can be continued more than three stages. Byinputting the fact that there is excess or deficiency at the time of theconfirmation of the corrected region, it is possible to cope with thechange in luminance even more than three stages.

Also, the generation of the corrected region and the storage in theregion storage unit may be all executed before display of the initialregion, may be executed in parallel to the display process, or may beexecuted after the fact that there is excess or deficiency is input. Asthe extraction of the next plausible region of the initial region, forexample, there is a method of changing a parameter of the extractionalgorithm used at the time of the extraction of the initial region andsetting a region boundary on the further outer side. Since the correctedregion is generated based on the initial region, the extraction processcan be completed in a shorter time than a time when a process ofextracting a plurality of regions is executed. Of course, the extractionprocess is also applicable even when an extraction target region hasluminance higher than that of its periphery region.

As a method of setting the correction threshold value, a method may beused in which a region correction process executing unit has a parametertable illustrated in FIG. 5 in advance and a threshold value may be setwith reference to the parameter table, or a method may be used in whicha parameter is used in the form of a formula to set a correctionthreshold value, as in an example of a parameter table illustrated inFIG. 6. Here, f1, f2, f3, f′1, f′2, f′3, . . . are any preset functions,and “f1=f′1, f2=f′2, f3=f′3, . . . ” may be set or the functions may beindividually set.

Otherwise, parameters themselves are not defined, but an algorithmexecution result may be retained as a table. For example, FIG. 7illustrates an example of a correction process item table. A convergencecondition is determined, for example, when all of the correction itemsare satisfied with reference to the correction item table set in thisway, when one of the correction items is satisfied, or when a numberequal to or greater than a given number of condition items is satisfied.

Next, a case will be described in which not an extraction region in theform faithful to luminance but an extraction region in the formscreening the luminance is required. That is, correct extraction can beexecuted in the range of a region of interest, but a form close to asphere or a more smooth extraction shape is preferable depending on itsuse scene in some cases. In this case, an extraction algorithm orparameters thereof is not changed, but a shape is gradually changed anddisplayed in a form which does not considerably depart from the initialregion. That is, approximation using polygonal approximation or discretesine transform is executed on a region contour or a method of forming asmooth contour using a morphological filter is used. A method ofchanging parameters according to one scheme is used, or a plurality ofcontours are generated by applying a plurality of schemes and likelihoodis considered to be different depending on an application scene.However, as one method of a general case, there is exemplified a methodof setting an order of changing a form from a form closest to an initialregion to a 3-dimensional geometric figure such as a sphere step by stepas far as possible along a plausibility decreasing axis. The decrease inthe plausibility is defined in advance as a rule and the regioncorrection is executed such that the shape of an extracted region ischanged according to the rule.

Here, a case in which the decrease in the plausibility is defined as anorder of changing a region shape from an initial region to an ovalsphere will be exemplified specifically. First, an initial region isextracted through an automatic extraction scheme. The automaticextraction scheme herein may use the above-described region growingmethod or an algorithm such as LevelSet, or may be a scheme such as abinarization process using a threshold value depending oncharacteristics of an image.

An algorithm of filling a blank portion of the region inside ofcorrected region of the initial region obtained in this way is firstconsidered. A case in which a morphological filter or the like is usedwill be described as an example of the algorithm for the filling withreference to FIG. 8.

FIG. 8 is an image diagram illustrating example of regions corrected bythe region correction process executing unit 32 from initial regionsextracted by the automatic extraction process executing unit 34,including a correction procedure. First, a Dilation process is executeduntil blank portions inside an initial region B illustrated on the leftof FIG. 8 are filled. The Dilatiob process is a process of growing aregion contour pixel by pixel. A state in which the blank portionsinside the initial region B are all filled through this process isillustrated as a region during correction in the middle of FIG. 8.Thereafter, a region obtained by executing an Erosion process by thesame number as the number of times the Dilation process is executed isillustrated as a corrected region A on the right of FIG. 8. The Erosiobprocess is a process of executing a reverse manipulation of the Dilationprocess and is a process of contracting the contour of a region pixel bypixel. The corrected region A in which the blank portions inside theinitial region B are all filled in this way is generated and thiscorrected region is displayed here as the next plausible region of theinitial region B.

An example of a method of approximating a corrected region A to a moreoval sphere will be described with reference to FIG. 9. FIG. 9 is animage diagram illustrating examples of a plurality of corrected regionscorrected by the region correction process executing unit 32. A regionobtained by replacing a concave portion of the contour of a correctedregion A illustrated on the left of FIG. 9 with a straight line is acorrected region B in the middle of FIG. 9, and an ellipse in which themajor diameter and the minor diameter of an ellipse of the initialregion are set to the long axis and the short axis is a corrected regionC on the right of FIG. 9. In this way, the corrected region is changedin the order of the corrected region A→the corrected region B→thecorrected region C. Also, the description in FIGS. 4, 8, and 9 has beenmade on the assumption that an extracted region is a 2-dimensionalregion. However, even when an extracted region is a 3-dimensionalregion, a corrected region can be generated according to the samealgorithm.

REFERENCE SIGNS LIST

-   10 external storage device-   11 image processing device-   12 display device-   13 input device-   20 internal memory-   21 image processing algorithm executing unit-   22 input manipulation acquiring unit-   31 visualization process executing unit-   32 region correction process executing unit-   33 extracted region storage unit-   34 automatic extraction process executing unit

The invention claimed is:
 1. An image processing device executing imageprocessing on volume data of a 3-dimensional image, the image processingdevice comprising: an extraction process executing unit that extracts a3-dimensional initial region satisfying a condition given in advancefrom the volume data; a region correction process executing unit thatextracts a 3-dimensional corrected region by performing a correctionprocess on the initial region; and a visualization process executingunit that generates a plurality of cross-sectional diagrams of the3-dimensional image from the volume data and outputs at least some ofthe plurality of cross-sectional diagrams, wherein when the initialregion is displayed, the visualization process executing unit selectsone or more cross-sectional diagrams having voxels included in theinitial region from the plurality of cross-sectional diagrams generatedby the visualization process executing unit, and outputs the selectedone or more cross-sectional diagrams so that the voxels included in theinitial region are distinguishable from other regions, when thecorrected region is displayed, the visualization process executing unitselects one or more cross-sectional diagrams having voxels included inthe corrected region from the plurality of cross-sectional diagramsgenerated by the visualization process executing unit, and outputs theselected one or more cross-sectional diagrams so that the voxelsincluded in the corrected region are distinguishable from other regions,and wherein, when the corrected region is displayed after the initialregion has been displayed and a first number of cross-sectional diagramshaving voxels included in the initial region is different from a secondnumber of cross-sectional diagrams having voxels included in thecorrected region, the visualization process executing unit changes anumber of cross-sectional diagrams to be output from the first number tothe second number.
 2. The image processing device according to claim 1,wherein, when the corrected region is displayed after the display of theinitial region and 3-dimensional regions of the initial region and ofthe corrected region are different in size, the visualization processexecuting unit changes the number of cross-sectional diagrams to beoutput from the first number to the second number.
 3. The imageprocessing device according to claim 1, wherein the region correctionprocess executing unit extracts the corrected region by changing aparameter given as the condition.
 4. The image processing deviceaccording to claim 1, wherein the region correction process executingunit extracts the corrected region so that the region is changed inaccordance with a rule determined in advance.
 5. The image processingdevice according to claim 1, wherein the region correction processexecuting unit extracts the corrected region by correcting a contour ofthe initial region.
 6. A method of executing image processing on volumedata of a 3-dimensional image by an image processing device, the methodcomprising: extracting a 3-dimensional initial region satisfying acondition given in advance from the volume data by an extraction processexecuting unit of the image processing device; extracting a3-dimensional corrected region by performing a correction process on theinitial region by a region correction process executing unit of theimage processing device; generating a plurality of cross-sectionaldiagrams of the 3-dimensional image from the volume data by avisualization process executing unit of the image processing device;selecting one or more cross-sectional diagrams having voxels included inthe initial region from the plurality of cross-sectional diagramsgenerated by the visualization process executing unit, and outputtingthe selected one or more cross-sectional diagrams so that the voxelsincluded in the initial region are distinguishable from other regions bythe visualization process executing unit; and selecting one or morecross-sectional diagrams having voxels included in the corrected regionfrom the plurality of cross-sectional diagrams generated by thevisualization process executing unit, and outputting the selected one ormore cross-sectional diagrams so that the voxels included in thecorrected region are distinguishable from other regions by thevisualization process executing unit, wherein, when the corrected regionis displayed after the display of the initial region and a first numbercross-sectional diagrams having the voxels included in the initialregion is different from a second number of cross-sectional diagramshaving the voxels included in the corrected region, the visualizationprocess executing unit changes a number of cross-sectional diagrams tobe output from the first number to the second number.
 7. The methodaccording to claim 6, wherein, when the corrected region is displayedafter the display of the initial region and 3-dimensional regions of theinitial region and the corrected region are different in size, thevisualization process executing unit changes the number ofcross-sectional diagrams to be output from the first number to thesecond number.
 8. The method according to claim 6, wherein the regioncorrection process executing unit extracts the corrected region bychanging a parameter given as the condition.
 9. The method according toclaim 6, wherein the region correction process executing unit extractsthe corrected region so that the region is changed in accordance with arule determined in advance.
 10. The method according to claim 6, whereinthe region correction process executing unit extracts the correctedregion by correcting a contour of the initial region.